MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.
Yang-Ming LinChing-Tai ChenJia-Ming ChangPublished in: BMC genomics (2019)
We showed that MS2CNN outperforms MS2PIP for 2+ and 3+ peptides and pDeep for 3+ peptides. This implies that MS2CNN, the proposed convolutional neural network model, generates highly accurate MS2 spectra for LC-MS/MS experiments using Orbitrap machines, which can be of great help in protein and peptide identifications. The results suggest that incorporating more data for deep learning model may improve performance.
Keyphrases
- convolutional neural network
- ms ms
- deep learning
- mass spectrometry
- multiple sclerosis
- amino acid
- liquid chromatography
- high resolution
- artificial intelligence
- liquid chromatography tandem mass spectrometry
- high performance liquid chromatography
- ultra high performance liquid chromatography
- gas chromatography
- big data
- high resolution mass spectrometry
- tandem mass spectrometry
- small molecule